Open-source low code data preparation library in python. Collect, clean and visualization your data in python with a few lines of code.

Make your dataset talk to you. The AI assistant for data preparation.

A Holistic Platform for Automating Data Preparation

Trifacta Flows Examples and Templates. Flows zip files, recipes and datasets.

This repo includes codes for ML Zoomcamp. If you can follow the tutorials from the link here: https://www.youtube.com/watch?v=rowoDjPc8HU&list=PL3MmuxUbc_hIhxl5Ji8t4O6lPAOpHaCLR 👩🏼‍💻

mltrons dptron: Dirty Data in, Clean Data Out!

Learn data visualization through Tableau 2020 and create opportunities for you or key decision-makers to discover data patterns such as customer purchase behavior, sales trends, or production bottlenecks. This Course on Udemy

Public repository for custom blocks for Omniscope

Data Science materials

This repository demonstrates data imputation using Scikit-Learn's SimpleImputer, KNNImputer, and IterativeImputer.

The project deals with determining and predicting the type of accident taking place in the city of Austin. The data would help in understanding what possible factors are leading to the accidents based on the severity of the incident that has occurred.

Trying to predict survival rate of passengers using algorithms like Logistic Regression, Ada Boost, Gradient Boost , Decision Tree Classifiers , Extra Tree Classifiers , Random Forest Classifiers and XG Boost with appropriate data preprocessing techniques.

CAN U PREDICT CORRECTLY WHETHER A CUSTOMER WILL CANCEL THE RESERVATION?? You can find the dataset from this kaggle website: https://www.kaggle.com/datasets/ahsan81/hotel-reservations-classification-dataset

The data is available in kaggle competitions. https://www.kaggle.com/competitions/spaceship-titanic I have participated and completed the competition on my own.

This project involves the use of K-Means Clustering to find the best accommodation for students in Bangalore (or any other city of your choice) by classifying accommodation for incoming students on the basis of their preferences on amenities, budget and proximity to the location.

In this project, I have used logistic regression, a supervised machine learning algorithm, to predict whether a person has diabetes or not based on various features such as age, blood pressure, glucose level, body mass index, etc. I have used Python and popular libraries such as Pandas, Scikit-Learn, and Matplotlib to perfom model building

For a real estate firm, building a house price prediction model based upon various factors. Problem - Regression | Algorithm used -Linear Regression using OLS

Preparing and Cleaning Data

Crime and Incarceration in the United States contain data on crimes that are committed, and the prisoner counts in every 50 states, for which the data is analyzed using various analytical methods.

This is my 6th semester Essentials of Data Analytics project.